Abstract. When applying a quantile mapping-based bias correction to daily temperature extremes simulated by a global climate model (GCM), the transformed values of maximum and minimum temperatures are changed, and the diurnal temperature range (DTR) can become physically unrealistic. While causes are not thoroughly explored, there is a strong relationship between GCM biases in snow albedo feedback during snowmelt and bias correction resulting in unrealistic DTR values. We propose a technique to bias correct DTR, based on comparing observations and GCM historic simulations, and combine that with either bias correcting daily maximum temperatures and calculating daily minimum temperatures or vice versa. By basing the bias correction on a base period of 1961-1980 and validating it during a test period of 1981-1999, we show that bias correcting DTR and maximum daily temperature can produce more accurate estimations of daily temperature extremes while avoiding the pathological cases of unrealistic DTR values.

When applying a quantile-mapping based bias correction to daily temperature extremes simulated by a global climate model (GCM), the transformed values of maximum and minimum temperatures are changed, and the diurnal temperature range (DTR) can become physically unrealistic. While causes are not thoroughly explored, there is a strong relationship between GCM biases in snow albedo feedback during snowmelt and bias correction resulting in unrealistic DTR values. We propose a technique to bias correct DTR, based on comparing observations and GCM historic simulations, and combine that with either bias correcting daily maximum temperatures and calculating daily minimum temperatures or vice versa. By basing the bias correction on a base period of 1961–1980 and validating it during a test period of 1981–1999, we show that bias correcting DTR and maximum daily temperature can produce more accurate estimations of daily temperature extremes while avoiding the pathological cases of unrealistic DTR values

EVALUATION OF THE CMIP5 DECADAL HINDCASTS IN THE STATE OF CALIFORNIA by Colin J. McKellar This study investigated the ability of the new Coupled Model Inter-comparison Project phase 5 (CMIP5) decadal hindcasts to predict the observed decadal variability for maximum temperature (Tmax) and minimum temperature (Tmin) in California over two historical periods in the 20 th century and one future period in the 21 st century. Annual and seasonal California temperature trends were computed by averaging 54 United States Historical Climate Network version 2 temperature observations from 1960-1990 and 1980-2010. Modeled California temperatures were reconstructed with bi-linear interpolation from the CMIP5 decadal hindcasts and 20 th century experiments. The individual model ensemble averages (MEA) and mean model ensemble averages (MMEA) were then compared to the observations during 1960-1990 and 1980-2010. The decadal hindcasts displayed a similar overall skill as the 20 th century experiments in predicting the observed annual and seasonal temperature trends during both historical periods. However, the predictive skill for individual models showed that the decadal hindcasts systematically improved the MEA predictions and that certain models, such as the MRI-CGCM3, outperformed the MMEA in each experiment. Also the higher performing models, such as the MRI-CGCM3, provided better future Tmax and Tmin trend predictions. Future predictions show increasing annual and seasonal temperature trends that indicate a longer growing season by the year 2035. v ACKNOWLEDGEMENTS I would first like to thank the members of my thesis committee: Dr. Eugene Cordero, Dr. Allison Bridger, and Dr. Bridget Thrasher. They continuously pushed me to realize my full potential and have been encouraging and positive through each step of graduate school. Furthermore, they have selflessly taken their own time and energy to help me advance my skills and knowledge of meteorology and as a scientist. I would also like to thank all of the faculty members in the Department of Meteorology and Climate Science. Each has always been there for advice and help and has fostered an open positive environment for learning and personal growth. I would also thank my undergraduate professors at the University of Northern Colorado, particularly Dr. Paul Nutter and Dr. Cindy Shellito who shared and passed on their passion for meteorology. I would like to thank all my friends I have made here at SJSU; Terrence Mullens, Henry Bartholomew, Laura Hodgens, and Rachel Eidelman, and friends back in Colorado, Lisa Coco and Adam Sturtz, to name a few. They have always been there to help me and listen through the good times and bad. Lastly, I want to thank my family for the love and support through my journey. vi